Modelling palaeoecological datasets using a state-space approach: two case-studies

• Jack Williams
• Tony Ives
• Angie Perotti
• Nora Schlenker

• David Nelson
• Bryan Schuman
• Jonathan Johnson
• Vania Stefanova

• ESA 2023: presented early development
• ESA 2024: ran workshop on how to fit multinomialTS
• ESA 2025!: presenting developed use-cases

• Do you have multinomially distributed data?
• I’ll try to leave a couple of extra minutes for discussion.

• Models a multinomial distribution (i.e., count data) directly
• Accepts multiple covariates of different type
• Incorporates process error and observation error
• Asena et al., in review

Tulane

• Florida
• ~60,000 year-long record
• Centennial to millennial dynamics
• Covariates
  • fungal spores (proxy for megaherbivory)
  • \(CO_2\) \(\delta18O\) (climate)
  • Heinrich events (climate-related)
  • charcoal (fire)
• Williams et al., in prep, Grimm et al. (1993); Grimm et al. (2006)

Sunfish

• Pennsylvania
• ~13,000 years
• Decadal to centennial dynamics
• Covariates
  • lake level (proxy for humidity)
• Johnson et al., in review

Questions/hypotheses:

• are biotic interactions or climatic variability the primary drivers of change?
• is the holocene period significantly different to the full 60,000 years?

Parameter selection

• window span / prediction resolution 200 years
• species/taxonomic group selection
  • two functional groups
  • two key dominant species
• interactions estimated:
  • no interactions
  • pine - oak

Questions/hypotheses:

• Are local conditions (humidity) a stronger driver of change than species interactions?

Parameter selection

• window span / prediction resolution 100 years
• species/taxonomic group selection
  • five most dominant species
  • Eastern white pine, hemlock, beech, oak, and birch
• interactions:
  • estimated between pine and hemlock (for this example)